Description: Through a combination of classic papers and more
recent work, the course explores automated decision making from a
computer-science perspective. It examines efficient algorithms, where
they exist, for single agent and multiagent planning as well as
approaches to learning near-optimal decisions from experience. Topics
include Markov decision processes, stochastic and repeated games,
partially observable Markov decision processes, and reinforcement
learning. Of particular interest will be issues of generalization,
exploration, and representation. Students will replicate a result in
a published paper in the area. Participants should have taken a
graduate-level machine-learning course and should have had some
exposure to reinforcement learning from a previous computer-science
class or seminar; check with instructor if not sure.

Prerequisites: CSCI 1950F or CSCI 1420 or permission of the instructor.

Online quizzes: There will be one online quiz per class to solidify the concepts.

Result replication presentation: Students will form into small
groups of two to four, and select a relevant paper from the literature.
They will choose a graph in the paper and create an independent
implementation/replication of this result. Students often find that
important parameters needed to replicate the result are not stated in
the paper and that obtaining the same pattern of results is sometimes
not possible. Students will present their work at the end of the
semester. Grades are based on the fidelity of the replication (25%),
how well they show they understand the original paper (25%), the
quality of the presentation itself in terms of clarity and creativity
(25%), and their short written report (25%). The grade on this
project will represent 50% of the final grade in the class.

BURLAP: We will try to use and extend BURLAP, the Brown-UMBC
Reinforcement Learning and Planning system, to learn about the
algorithms in the class and for the result replications.

9/26[quiz closed]: Convergence proof of VI.
Read Littman
and Szepesvári (1996). Q-learning. The absolute difference between the
kth order statistic of two lists is less than the pairwise absolute
differences between the lists.